Parkinson's disease is a progressive disorder that affects the nervous system and the parts of the body controlled by the nerves. Symptoms are also not that sound to be noticeable. Signs of stiffening, tremors, and slowing of movements may be signs of Parkinson's disease. But there is no ascertain way to tell whether a person has Parkinson's disease or not because there are no such diagnostics methods available to diagnose this disorder. But what if we use machine learning to predict whether a person suffers from Parkinson's disease or not?

The vendors who are selling everyday items need to keep their stock up to date so, that no customer returns from their shop empty hand. In this article, we will try to implement a machine learning model which can predict the stock amount for the different products which are sold in different stores. Python libraries make it easy for us to handle the data and perform typical and complex tasks with a single line of code. Now let's load the dataset into the panda's data frame and print its first five rows. Now let's check the size we have calculated is correct or not .

Machine learning proves immensely helpful in many industries in automating tasks that earlier required human labor one such application of ML is predicting whether a particular trade will be profitable or not. In this article, we will learn how to predict a signal that indicates whether buying a particular stock will be helpful or not by using ML. Let's start by importing some libraries which will be used for various purposes which will be explained later in this article. Python libraries make it very easy for us to handle the data and perform typical and complex tasks with a single line of code. The dataset we will use here to perform the analysis and build a predictive model is Tesla Stock Price data.

K-Nearest Neighbours is one of the most basic yet essential classification algorithms in Machine Learning. It belongs to the supervised learning domain and finds intense application in pattern recognition, data mining and intrusion detection. It is widely disposable in real-life scenarios since it is non-parametric, meaning, it does not make any underlying assumptions about the distribution of data (as opposed to other algorithms such as GMM, which assume a Gaussian distribution of the given data). We are given some prior data (also called training data), which classifies coordinates into groups identified by an attribute. Now, given another set of data points (also called testing data), allocate these points a group by analyzing the training set.

LOANS are the major requirement of the modern world. By this only, Banks get a major part of the total profit. It is beneficial for students to manage their education and living expenses, and for people to buy any kind of luxury like houses, cars, etc. But when it comes to deciding whether the applicant's profile is relevant to be granted with loan or not. Banks have to look after many aspects.

CART( Classification And Regression Tree) is a variation of the decision tree algorithm. It can handle both classification and regression tasks. Scikit-Learn uses the Classification And Regression Tree (CART) algorithm to train Decision Trees (also called "growing" trees). CART was first produced by Leo Breiman, Jerome Friedman, Richard Olshen, and Charles Stone in 1984. CART is a predictive algorithm used in Machine learning and it explains how the target variable's values can be predicted based on other matters.

We all have experienced a time when we have to look up for a new house to buy. But then the journey begins with a lot of frauds, negotiating deals, researching the local areas and so on. So to deal with this kind of issues Today we will be preparing a MACHINE LEARNING Based model, trained on the House Price Prediction Dataset. As we have imported the data. So shape method will show us the dimension of the dataset.

We have come so far in technology that things that used to be fiction for us have become a new normal in our day-to-day life. You possibly wouldn't have imagined how a robot could work as a waiter in a restaurant to serve a café, right? But today, we have passed all of those barricades. Thanks our innovations and the contributions, that they have given so far are impeccable. The era of machine learning and artificial intelligence has already begun and believe it or not it has been quite an impressive journey so far and its application has also made a significant impact in our lives.

The automatic translation of text from one natural language (the source) to another is known as machine translation (the target). It was one of the first applications for computers that were imagined (Weaver, 1949). The translation is tough since it necessitates a thorough understanding of the text in the most general scenario. This is true even for very basic messages, such as one-word "texts." Consider the word "Open" on a store's front door.

It is true to say that the introduction of Artificial Intelligence, also known as AI models are a blessing to the IT industry. It has very well connected humans and the natural environment with technology in such a way that no one has ever expected. Machines have got enough power to replace humans. But, now the question arises is what is AI (Artificial Intelligence)? If it's playing an important role in the IT sector then why it's taking so much time to take over all the security management of the industry?